{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  rating  timestamp\n",
       "0      196      242       3  881250949\n",
       "1      186      302       3  891717742\n",
       "2       22      377       1  878887116\n",
       "3      244       51       2  880606923\n",
       "4      166      346       1  886397596"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列名\n",
    "column_name_s = ['user_id', 'item_id', 'rating', 'timestamp']\n",
    "# 读取数据\n",
    "df_data = pd.read_csv('./data/u.data', sep='\\t', names=column_name_s, encoding='latin-1')\n",
    "# 打印前5行\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50     583\n",
       "258    509\n",
       "100    508\n",
       "181    507\n",
       "294    485\n",
       "Name: item_id, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到没饿item_id出现的次数\n",
    "items_rating_times = df_data['item_id'].value_counts()\n",
    "# 查看前5行\n",
    "items_rating_times.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    583\n",
       "1    509\n",
       "2    508\n",
       "3    507\n",
       "4    485\n",
       "Name: item_id, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看索引，便于画图\n",
    "items_rating_times.index = range(items_rating_times.count())\n",
    "items_rating_times.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(items_rating_times.index, items_rating_times)\n",
    "plt.xlabel('Item Index')\n",
    "plt.ylabel('Rating times')\n",
    "plt.title('Item Rating Times')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
